The present invention relates generally to the field of data processing systems for analyzing biological microarrays. More particularly, the invention relates to techniques for integrating data derived from multiple images made of such microarrays.
An increasing number of applications have been developed for biological microarrays. Such microarrays typically include deoxyribonucleic acid (DNA) and ribonucleic acid (RNA) probes. These are specific for nucleotide sequences present in genes in humans and other organisms. In certain applications, for example, individual DNA and RNA probes can be attached at small locations in a geometric grid (or randomly) on a microarray support. A test sample, such as from a known person or organism, can be exposed to the grid, such that complimentary genes of fragments hybridize to probes at the individual sites in the array. The array can then be examined by scanning specific frequencies of light over the sites to identify which genes or fragments in the sample are present, by fluorescence of the sites at which genes or fragments hybridized.
In similar applications, biological microarrays may be used for genetic sequencing and similar applications. In general, genetic sequencing consists of determining the order of nucleotides or nucleic acid in a length of genetic material, such as a fragment of DNA or RNA. Relatively short sequences are typically analyzed, and the resulting sequence information may be used in various bioinformatics methods to logically fit fragments together so as to reliably determine the sequence of much more extensive lengths of genetic material from which the fragments were derived. Automated, computer-based examination of characteristic fragments have been developed, and have been used more recently in genome mapping, identification of genes and their function, and so forth.
For these and other applications of biological microarrays, improvements have recently been made in imaging systems for capturing data related to the individual molecules attached at sites of the microarrays. For example, improvements in imaging systems allow for faster, more accurate and higher resolution scanning and imaging, particularly through the use of line-scanning and confocal control of imaging optics. However, as the density of microarrays increases, and the size of the areas containing individually characterized sites also increases, scanning, both by point scanning and line scanning approaches becomes problematic. In particular, depending upon the limitations of the scanner, a pre-defined area of a microarray area or site grid may be too large to be scanned in a single pass by the scanning system. Consequently, information gathered from a scanning pass will be incomplete unless associated with that of other scanning passes for the overall area.
Conventional techniques may be used to piece together images of the scanned regions of microarrays. However, such techniques require extensive memory and computational capacities. Simply stitching images together does not result in a time or computationally efficient approach to analysis of the image data acquired of the microarrays.
There is a need, therefore, for an improved technique for analyzing image data in multiple imaging passes over a biological microarray. There is a particular need for techniques that will allow rapid and accurate integration of data from multiple imaging passes, permitting improved throughput for diagnostic, encoding, sequencing, and other operations performed with the microarrays.